The present invention relates to medical imaging of the heart, and more particularly, to automatic segmentation of the aorta in 3D medical images, such as C-arm CT volumes.
Aortic valve disease is the most common valvular disease in developed countries, and has the second highest incidence among congenital valvular defects. Implantation of an artificial valve is often necessary to replace a damaged natural valve. Before surgery to replace a damaged valve, several important parameters of the aortic valve need to be extracted for surgery planning. For example, the diameter of the aortic valve annulus needs to be measured accurately in order to select an appropriately sized artificial valve. The distance from the coronary ostia to the sinutubular junction is also an important measure in order to place the artificial valve at an appropriate position to avoid blocking the blood flow to the coronary arteries. During a valve implantation surgery, 2D fluoroscopic images are often captured in real time using a C-arm image acquisition system to provide guidance to the cardiologist. When there is no contrast agent applied, the aortic root cannot be clearly distinguished from the background in the fluoroscopic images. Overlaying a patient-specific aorta model onto the fluoroscopic images during the surgery is often helpful to monitor the relative position of a catheter with respect to the aortic valve.
Computed tomography (CT) is typically used to capture a 3D volume, which is used to perform the necessary 3D measurements and 2D/3D overlay. However, recently, C-arm CT is emerging as a new imaging technique with many advantages as compared to conventional CT. A C-arm CT volume is generated by rotating the X-ray detector of a C-arm image acquisition system. Since both the 3D C-arm CT volume and the 2D fluoroscopic images can be captured on the same device (C-arm system) within a short time interval, overlay of a 3D patient-specific aorta model from a C-arm CT volume on the 2D fluoroscopic images can be easily and accurately implemented. Furthermore, an extracted aortic root resulting from a 3D C-arm CT image can provide the C-arm angulation which will result in the best view of the coronary tree for acquiring the 2D fluoroscopic images.
Although C-arm CT has many advantages over conventional CT with respect to artificial valve implantation surgery, automatic segmentation of the aorta in a C-arm CT volume is a challenging problem. First, the image quality of a C-arm CT volume is typically not as good as a cardiac CT volume due to reconstruction artifacts and a large variation in the use of contrast agent. For example, some C-arm CT volumes may have high contrast, while others have low contrast due to improper timing. A simple intensity-based thresholding technique does not work for volumes having low contrast. Accordingly, such a thresholding technique is not reliable for aorta segmentation in C-arm CT volumes. Second, the scanning protocol for C-arm CT volumes can be quite diverse in the capture range. That is, in different C-arm CT volumes, different portions of the aorta may or may not be present. For example, in some volumes only the ascending aorta is visible, while in other volumes both the ascending and descending aorta are visible. A statistical shape model is often used in object segmentation to enforce a prior shape constraint so that the final segmentation converges to a reasonable shape. Due to the variation in the aorta shape in various C-arm CT volumes, a statistical shape model cannot be applied directly for aorta segmentation.